{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**复习：**回顾学习完第一章，我们对泰坦尼克号数据有了基本的了解，也学到了一些基本的统计方法，第二章中我们学习了数据的清理和重构，使得数据更加的易于理解；今天我们要学习的是第二章第三节：**数据可视化**，主要给大家介绍一下Python数据可视化库Matplotlib，在本章学习中，你也许会觉得数据很有趣。在打比赛的过程中，数据可视化可以让我们更好的看到每一个关键步骤的结果如何，可以用来优化方案，是一个很有用的技巧。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 第二章：数据可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 开始之前，导入numpy、pandas以及matplotlib包和数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载所需的库\n",
    "# 如果出现 ModuleNotFoundError: No module named 'xxxx'\n",
    "# 你只需要在终端/cmd下 pip install xxxx 即可\n",
    "%matplotlib  inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  PassengerId  Survived  Pclass  \\\n",
       "0           0            1         0       3   \n",
       "1           1            2         1       1   \n",
       "2           2            3         1       3   \n",
       "3           3            4         1       1   \n",
       "4           4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#加载result.csv这个数据\n",
    "text = pd.read_csv(r'result.csv')\n",
    "text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.7 如何让人一眼看懂你的数据？\n",
    "《Python for Data Analysis》第九章"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.7.1 任务一：跟着书本第九章，了解matplotlib，自己创建一个数据项，对其进行基本可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】最基本的可视化图案有哪些？分别适用于那些场景？（比如折线图适合可视化某个属性值随时间变化的走势）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "线型图、柱状图、盒形图、散布图、等值线图等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1)\n",
    "data = pd.Series(np.random.rand(16), index=list('abcdefghijklmnop'))\n",
    "data.plot.bar(ax=axes[0], color='k', alpha=0.7)\n",
    "data.plot.barh(ax=axes[1], color='k', alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1df9a9a6160>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JO7RoyA++04HnP1lD0dbdCepWRCR2CoIYFG3dzZi/zOOe6cs5v09b3powhMHdWh3z/saf0x3DeORdHRWISPAUBEfwWv46Rj44myXrtnHfZf14ZEx/mjWsV619Ht+8AVee0pGX8or4cvOuOHUqInJsFASHsL1kH7dP/ZQJU/PJbt2YNycM4fsD2x/xBeFY3XJ2N+qlGQ++80Vc9icicqwUBFWYv3oLIx+YzeuLNnDHsB68eNNpdGzZMK41WjfN4NrTOvOPT9dRsHFnXPctInI0FASV7Cuv4L4Zy7n88Y9Iq2O8NO40JgzLpm5aYv6ZbhrSlYx6aTzw9oqE7F9EJBYKgqhVm3Zx6aSPePjdAi4Z0J43JpzJgI7HJbRmy8b1+eHpXfjnog0s27A9obVERA4l9EHg7rwwfw3ffWg2qzft4tGrBnDvZf1oXD85n77xozO70iSjLn+eqaMCEQlGqD9raOuuUu56ZRHTl3zN4G4tue8H/WjXrEFSe2jWsB4/OrMr989cwWdF2zipfbOk1hcRCe0Rwewvihnx4Cze/Xwjvxh1As/eeErSQ+BbN5zemeYN63HfzOWB1BeRcAtdEJTsK+e//xm5cEyTjHq8esvpjB3SjTp14nNa6LFoklGPm4Z04/3lxeR9uSWwPkQknEIVBMu/2sFFEz/kiTmruPa0Trw+/gz6ZNWMp2KuG9yJVo3TuW+GXisQkeQKRRC4O09+uP/CMVOuz+F3o/vQID3tyBsnScP0utx8dnfmrtzMRys3B92OiIRIwoPAzDqY2XtmtszMlpjZhETXrGzjjhKuf3I+v319KWd0b8WbE4Zw7gltktlCzK46pSNtm2Zw/8zluHvQ7YhISCTjiKAMuNPdewGnAreaWe8k1GXm0q8Z8cBs5hVGLhzzxHU5ZDapuVcGy6iXxq3ndmf+6q3M+mJT0O2ISEgkPAjcfYO7L4h+vwNYBmQlsubu0jJ+8epn/OjpXNo2zeBfPz4jpgvH1ASX53Qgq3kD7p+howIRSY6kvkZgZp2B/sDHVSwba2a5ZpZbXFx8zDU+K9rGBQ/N4flP1nDTkK68eutgurducuxNJ1l63TpMGJrNwqJtvLNsY9DtiEgIJC0IzKwx8DJwu7sf9HkK7j7Z3XPcPSczM/Oo919e4Tz6fgEXP/ohu0ujF44Z1Yv6dWvOC8KxumRAFp1bNuS+mSuoqNBRgYgkVlKCwMzqEQmB59z9lXjvf903exjzl3n86a3lnH9iW966/cxqXTgmaHXT6jBhWDbLNmznrSVfBd2OiNRyyThryIAngGXufn+89z9t4XpGPDCLJeu2ce9l/Xjkyv40b5ge7zJJd2G/LLq3bsz9M1dQrqMCEUmgZBwRnA5cA5xrZvnRr1HV3en2kn3c8UI+P37+U7q3bswbE87k0jheOCZoaXWMO4b1oGDjTl5fuD7odkSkFkv4h865+xwgrr+d56/ewu1T89mwbQ+3D8tm/DndE3bNgCCN7NOWXu2a8sDbK7igb7ta+RgTYe7KTfRu17RWHBmKJENK/WapfOGYOnXgpXGDuX1Yj1r7C7JOHeMnw3uwevNuXlmwLuh2UsKKr3dw41O5/Oq1JUG3IpIyUuZjqL/cvIsfT81n4dpv+P6A9vzmwt40yajeReRTwbBerenXvhkPvvMFF/XPIr1u7Qy9eNheso+bnsmjcUZd/s93ewXdjkjKSJnfKjtKyli3dQ8TrxzAfT/oF4oQADAzfnJeT9Z9s4cXctcG3U6NVVHh3PniQtZu2c3EKwfQumlG0C2JpIyUCYI+Wc2Y8/Nz+G7fdkG3knRDsluR0+k4Jr5bQMm+8qDbqZEe+2AlM5d+zS9G9WJQlxZBtyOSUlImCCDyWTxhZGbceV5Pvtpewt8+XhN0OzXOrBXF3DtjORf2O54bTu8cdDsiKSelgiDMTuvWksHdWvLo+wXsLi0Lup0ao2jrbiZM/ZQerZvwh++fVGtOHxZJJgVBCrnzvB5s2lnK0x99GXQrNULJvnJufnYBZeXOpGsG0jA9Zc59EKlRFAQpZGCnFpzdM5NJH6xkR8m+oNsJ3K9fW8Jn67bx58tPpkurRkG3I5KyFAQp5ifDe/DN7n08+eHqoFsJ1POfrOGF3LXcdm53hvWumRcaEkkVCoIU07d9c4b3bsNfZheybXc4jwry137Dr19bwpAemdw+rEfQ7YikPAVBCvrJ8B7sKCnjL7MLg24l6Tbv3Mstz+bRuml9Hrz8ZNLq6MVhkepSEKSgXu2a8t2+7Xjyw1Vs3rk36HaSpqy8gtue/5TNu0qZdPVAjmukzxISiQcFQYq6Y1g2e/aV8/is8BwV3DtjBXNXbub/XtSHPlnNgm5HpNZQEKSo7q2bcNHJWTz90Wo27igJup2Ee2vxBiZ9sJIrT+nIZTkdgm5HpFZREKSwHw/NZl+58+h7K4NuJaEKNu7kzhcXcnKH5vz6e72Dbkek1lEQpLDOrRpx2cD2/O3jNaz/Zk/Q7STEzr1ljHs2j4x6aTx29YCUvAa1SE2nIEhx48/tjuM88l5B0K3Enbvzv/++kMLinTx8ZX/aNWsQdEsitZKCIMW1P64hYwZ15MX5a1mzeXfQ7cTVX2YX8sZnX/HzEScwuFuroNsRqbUUBLXAred0J62O8dC7XwTdStzMXbmJP7z5OaNOasvYIV2DbkekVlMQ1AJtmmZw9amdeGVBEYXFO4Nup9o2bNvDbX/7lC6tGvGnS/vpE0VFEkxBUEvcfHY36tdN44G3U/uoYG9Z5BNFS/aV8/g1OTSur08UFUk0BUEt0apxfa4/vTOvL1rP8q92BN3OMfvd60vJX/sN917Wj+6tGwfdjkgoKAhqkbFndqVRel3+PHNF0K0ck5dy1/Lcx2u46ayujDwpfJckFQlKUoLAzEaY2XIzKzCzu5JRM4yOa5TOjWd04a0lX7F43bag2zkqi9dt45f/WMzgbi352Xk9g25HJFQSHgRmlgZMBEYCvYExZqa3hybIjWd2oVmDeil1VLB1Vynjns2jZaN0HhrTn7ppOlAVSaZk/MQNAgrcvdDdS4GpwOgk1A2lphn1GDukK+98vpEFa7YG3c4RlVc4E17IZ+P2vTx29UBaNa4fdEsioZOMIMgC1la6XRS9TxLk+sGdadkoPSWOCh58ewWzVhTzmwtP5OQOzYNuRySUkhEEVZ0E7getZDbWzHLNLLe4uDgJbdVejerXZdxZ3Zj9xSY+LtwcdDuH9PbSr3no3QJ+kNOeMYP0iaIiQUlGEBQBlX/K2wPrD1zJ3Se7e46752RmZiahrdrt6lM70bpJfe6buQL3g3I3cKs37eKOF/Ppk9WU343uozeNiQQoGUEwH8g2sy5mlg5cAUxLQt1Qa5Cexq3ndOeTVVv4sKBmHRXsLi3jpmfySKtjPHbVQDLq6RNFRYKU8CBw9zJgPDAdWAa86O5LEl1X4IpBHTi+WQb3zlheY44K3J27X/mMFRt38NAV/enQomHQLYmEXlLO03P3N9y9h7t3c/ffJ6OmQP26adw2NJv8td/w3vKNQbcDwFNzV/Na/np+el5PhvTQU4AiNYFO2K7lLh3Yno4tGnLfjOBfK5i/egu//9cyhvVqw81ndQu0FxHZT0FQy9VLq8OEodksWb+d6Uu+CqyPjdtLuOW5BXRo0ZD7L+9HnTp6cVikplAQhMBF/bPomtmI+2euoLwi+UcF+8oruOW5BewsKWPS1QNpmlEv6T2IyKEpCEIgrY5x+7AerPh6J/9cdNCZuwn3+38tI/fLrfzx0r70bNsk6fVF5PAUBCFxwUnt6NmmCQ++/QVl5RVJq/ta/jqemruaH57ehQv7HZ+0uiISOwVBSNSpY9wxvAeFm3bxj/zkHBUs27Cdn7+8iEFdWnD3qBOSUlNEjp6CIETOP7ENfbKa8uA7K9iX4KOCbXv2Me7ZPJpm1OORK/tTT58oKlJj6aczRMyMO4f3ZO2WPbyUW5SwOhUVzk9eyGfd1j08dvUAWjfJSFgtEak+BUHInN0zk/4dm/Pwu19Qsq88ITUeea+Adz7fyH9d0JuBnVokpIaIxI+CIGTMjJ+e15MN20qY+smauO///eUb+fPbK7i4fxbXntYp7vsXkfhTEITQ4G4tOaVLCya+v5I9pfE7Kli7ZTcTpubTs00T/ufik/SJoiIpQkEQQmbGnef1pHjHXp6Ztzou+yzZV85Nz+Th7jx+zUAapOsTRUVShYIgpAZ1acGZ2a2Y9EEhO/eWVWtf7s4vX13M0g3beeCKk+nUslGcuhSRZFAQhNid5/Vky65SnvpwVbX289zHa3h5QREThmZz7glt4tSdiCSLgiDETu7QnGG9WjN5ViHb9uw7pn0sWLOV376+hHN6ZjJhaHacOxSRZFAQhNwdw3uwvaSMJ+Yc/VFB8Y693PLsAto2y+DPl5+sTxQVSVEKgpA78fhmjOzTlilzVrF1V2nM25WVV3Db8wvYuruUSVcPpHnD9AR2KSKJpCAQ7hjeg12lZTw+qzDmbf40fTnzCrfwPxefxInHN0tgdyKSaAoCoUebJlzY73j+39zVFO/Ye8T1/7VoA5NnFXLNqZ34/sD2SehQRBJJQSAATBiaTWl5BY+9v/Kw633x9Q5+9veFDOjYnP+6oHeSuhORRFIQCABdMxtzSf8snv34S77aVlLlOjtK9nHTM3k0TE/j0asGkl5X4yNSG+gnWf7tx0OzqahwHnnvi4OWuTs/fWkhX27ZzSNXDqBtM32iqEhtoSCQf+vQoiGXf6cDL8xfy9otu/9j2aQPCpm+5GvuHnkCp3ZtGVCHIpIICgL5D+PP7Y6Z8fC7+48KPizYxD3TP+eCvu248YwuAXYnIomQ0CAws3vM7HMzW2Rmr5pZ80TWk+pr16wBV53SkZcXrGP1pl2s+2YPtz3/Kd1bN+aP3++rTxQVqYUSfUQwE+jj7n2BFcDdCa4ncXDz2d2ol2bcM305tzybR2lZBZOuHkij+nWDbk1EEiChQeDuM9z924+2nAfopPMU0LpJBted1pl/fbaBhUXbuO8H/eia2TjotkQkQZL5GsEPgTcPtdDMxppZrpnlFhcXJ7EtqcpNZ3Ujq3kDbh+Wzfkntg26HRFJIHP36u3A7G2gqt8Uv3T316Lr/BLIAS7xGArm5OR4bm5utfqS6quo8Fr5QXJmlufuOUH3IVJTVPtJX3cfdrjlZnYdcAEwNJYQkJqjNoaAiBwsoa/+mdkI4OfAWe6++0jri4hI8iX6NYJHgCbATDPLN7NJCa4nIiJHqdqvESSCmRUDX1axqBWwKcntxEuq9l4b++7k7pnJbEakJquRQXAoZpabqi/ypWrv6luk9tNHTIiIhJyCQEQk5FItCCYH3UA1pGrv6luklkup1whERCT+Uu2IQERE4kxBICIScikTBGY2wsyWm1mBmd0VdD+xMrMpZrbRzBYH3UuszKyDmb1nZsvMbImZTQi6p1iYWYaZfWJmC6N9/zbonkRSQUq8RmBmaUSuZzAcKALmA2PcfWmgjcXAzIYAO4Gn3b1P0P3EwszaAe3cfYGZNQHygItq+r+3Ra6a08jdd5pZPWAOMMHd5wXcmkiNlipHBIOAAncvdPdSYCowOuCeYuLus4AtQfdxNNx9g7sviH6/A1gGZAXb1ZF5xM7ozXrRr5r/l45IwFIlCLKAtZVuF5ECv5hqAzPrDPQHPg64lZiYWZqZ5QMbgZnunhJ9iwQpVYKgqs9D1l96CWZmjYGXgdvdfXvQ/cTC3cvd/WQiV8MbZGYp8XScSJBSJQiKgA6VbrcH1gfUSyhEn2N/GXjO3V8Jup+j5e7fAO8DI4LtRKTmS5UgmA9km1kXM0sHrgCmBdxTrRV90fUJYJm73x90P7Eys0wzax79vgEwDPg80KZEUkBKBIG7lwHjgelEXrh80d2XBNtVbMzseeAjoKeZFZnZjUH3FIPTgWuAc6PXkcg3s1FBNxWDdsB7ZraIyB8PM939nwH3JFLjpcTpoyIikjgpcUQgIiKJoyAQEQk5BYGISMgpCEREQk5BICIScgoCEZGQUxCIiITc/wdSz5+nf2CfEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#思考回答\n",
    "#这一部分需要了解可视化图案的的逻辑，知道什么样的图案可以表达什么样的信号b\n",
    "# data = np.arange(10)\n",
    "# plt.plot(data)\n",
    "#figute 不能单独绘图 要将subplot放入figure中\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(2,2,1)\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None,\n",
    "                wspace=None, hspace=None)\n",
    "plt.plot([1.5, 3.5, -2, 1.6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'My first matplotlib plot'), Text(0.5, 0, 'Stages')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(np.random.randn(50).cumsum(), 'k--')\n",
    "fig = plt.figure()\n",
    "ax= fig.add_subplot(1, 1, 1)\n",
    "ax.plot(np.random.randn(1000).cumsum())\n",
    "ticks = ax.set_xticks([0, 250, 500, 750, 1000])\n",
    "labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],rotation=30, fontsize='small')\n",
    "ax.set_title('My first matplotlib plot')\n",
    "ax.set_xlabel('Stages')\n",
    "props = {\n",
    "    'title': 'My first matplotlib plot',\n",
    "    'xlabel': 'Stages'\n",
    "}\n",
    "ax.set(**props)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.7.2 任务二：可视化展示泰坦尼克号数据集中男女中生存人数分布情况（用柱状图试试）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#代码编写\n",
    "\n",
    "sex = text.groupby('Sex')['Survived'].sum()\n",
    "sex.plot.bar()\n",
    "plt.title('survived_count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】计算出泰坦尼克号数据集中男女中死亡人数，并可视化展示？如何和男女生存人数可视化柱状图结合到一起？看到你的数据可视化，说说你的第一感受（比如：你一眼看出男生存活人数更多，那么性别可能会影响存活率）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female     81\n",
       "male      468\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#思考题回答\n",
    "text[text['Survived']==0].groupby('Sex')['Survived'].count()\n",
    "#死亡与性别有关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.7.3 任务三：可视化展示泰坦尼克号数据集中男女中生存人与死亡人数的比例图（用柱状图试试）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#代码编写\n",
    "# 提示：计算男女中死亡人数 1表示生存，0表示死亡\n",
    "\n",
    "text.groupby(['Sex','Survived'])['Survived'].count().unstack().plot(kind='bar',stacked='True')\n",
    "plt.title('survived_count')\n",
    "plt.ylabel('count')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【提示】男女这两个数据轴，存活和死亡人数按比例用柱状图表示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.7.4 任务四：可视化展示泰坦尼克号数据集中不同票价的人生存和死亡人数分布情况。（用折线图试试）（横轴是不同票价，纵轴是存活人数）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【提示】对于这种统计性质的且用折线表示的数据，你可以考虑将数据排序或者不排序来分别表示。看看你能发现什么？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#代码编写\n",
    "# 计算不同票价中生存与死亡人数 1表示生存，0表示死亡\n",
    "fare_sur = text.groupby(['Fare'])['Survived'].value_counts().sort_values(ascending=True)\n",
    "fig = plt.figure()\n",
    "fare_sur.plot(grid=True)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "#### 2.7.5 任务五：可视化展示泰坦尼克号数据集中不同仓位等级的人生存和死亡人员的分布情况。（用柱状图试试）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#代码编写\n",
    "# 1表示生存，0表示死亡\n",
    "pclass_sur = text.groupby(['Pclass'])['Survived'].value_counts()\n",
    "import seaborn as sns\n",
    "sns.countplot(x=\"Pclass\", hue=\"Survived\", data=text)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】看到这个前面几个数据可视化，说说你的第一感受和你的总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#思考题回答\n",
    "穷的人死的多\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.7.6 任务六：可视化展示泰坦尼克号数据集中不同年龄的人生存与死亡人数分布情况。(不限表达方式)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#代码编写\n",
    "\n",
    "#使用sidebar\n",
    "text.groupby(['Age','Survived'])['Survived'].count().unstack().plot(kind='bar',stacked='True')\n",
    "plt.title('survived_count')\n",
    "plt.ylabel('count')\n",
    "#使用了facegrid\n",
    "# facet = sns.FacetGrid(text, hue=\"Survived\",aspect=3)\n",
    "# facet.map(sns.kdeplot,'Age',shade= True)\n",
    "# facet.set(xlim=(0, text['Age'].max()))\n",
    "# facet.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df9e1d7970>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 699.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用了facegrid\n",
    "facet = sns.FacetGrid(text, hue=\"Survived\",aspect=3)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, text['Age'].max()))\n",
    "facet.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.7.7 任务七：可视化展示泰坦尼克号数据集中不同仓位等级的人年龄分布情况。（用折线图试试）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1df9e2792e0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#代码编写\n",
    "\n",
    "text.Age[text.Pclass == 1].plot(kind='kde')\n",
    "text.Age[text.Pclass == 2].plot(kind='kde')\n",
    "text.Age[text.Pclass == 3].plot(kind='kde')\n",
    "plt.xlabel(\"age\")\n",
    "plt.legend((1,2,3),loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】上面所有可视化的例子做一个总体的分析，你看看你能不能有自己发现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#思考题回答\n",
    "# 反应类别间比较的数据用柱状图 facegrid比较好\n",
    "# 反应趋势类的用折线图比较好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【总结】到这里，我们的可视化就告一段落啦，如果你对数据可视化极其感兴趣，你还可以了解一下其他可视化模块，如：pyecharts，bokeh等。\n",
    "\n",
    "如果你在工作中使用数据可视化，你必须知道数据可视化最大的作用不是炫酷，而是最快最直观的理解数据要表达什么，你觉得呢？"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
